Learning and Generalization in Overparameterized Normalizing Flows

This repository contains a pytorch code to replicate experiments in the paper: Learning and Generalization in Overparameterized Normalizing Flows.

Requirements

The code for synthetic datasets is tested on

• python 3.6.9
• pytorch 1.10.1
• matplotlib 3.3.4
• numpy 1.19.5

Overview

• Experiments of the paper is divided in two parts. Code files for Constrained Normalizing Flows (CNFs) are given in ./CNF/ folder. Code files for Unconstrained Normalizing Flows (UNFs) are given in ./UNF/ folder.
• All synthetic datasets used in the paper are given in ./datasets/ folder.
• Code to reproduce the results for the Miniboone dataset is given in ./BNAF/ and ./UMNN/.

Acknowledgement

For experiments on Miniboone datasets, we use the code from BNAF and UMNN.

Citation

If you find this project useful, please consider citing the following publication:

@article{shah2021learning,
  title={Learning and Generalization in Overparameterized Normalizing Flows},
  author={Shah, Kulin and Deshpande, Amit and Goyal, Navin},
  journal={arXiv preprint arXiv:2106.10535},
  year={2021}
}